metadata
language:
- sw
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_15_0
metrics:
- wer
model-index:
- name: swahili
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 15.0
type: mozilla-foundation/common_voice_15_0
config: lg
split: validation
args: 'config: lu, split: test'
metrics:
- name: Wer
type: wer
value: 36.497908126611165
swahili
This model is a fine-tuned version of openai/whisper-small on the Common Voice 15.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3824
- Wer: 36.4979
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.6697 | 0.1129 | 500 | 0.7159 | 64.0293 |
| 0.4719 | 0.2258 | 1000 | 0.5437 | 50.6878 |
| 0.4218 | 0.3388 | 1500 | 0.4773 | 45.0904 |
| 0.3896 | 0.4517 | 2000 | 0.4405 | 41.5501 |
| 0.3721 | 0.5646 | 2500 | 0.4173 | 39.9865 |
| 0.3386 | 0.6775 | 3000 | 0.3996 | 37.9094 |
| 0.3414 | 0.7904 | 3500 | 0.3883 | 37.3082 |
| 0.3078 | 0.9033 | 4000 | 0.3824 | 36.4979 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1